How an AI agent can produce a course quality audit with specific corrections
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant Heights Platform context, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent produce a course quality audit with specific corrections?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
Define the learner path
Name the course version, target learner, starting knowledge, intended outcome, and path through modules. Include publishing state, prerequisite rules, completion requirements, and assessment expectations.
The agent should trace the path and cite each lesson or setting behind a finding. It should identify missing context, duplicated material, jumps in difficulty, unclear actions, and dead-end progression.
Example starter prompt
Audit Heights Platform course [course/version] for [target learner] pursuing [outcome]. Follow path [modules] with prerequisites and completion rules [rules].
For each issue, cite the module or lesson, describe the learner impact, and propose the smallest correction. Check instructions, resources, progression, prerequisites, assessments, and completion. Separate draft and published content. Do not edit the course.Review from the learner’s starting point
Do not assume knowledge that the course never introduces. Check that resources exist and actions are possible in the stated order. A long lesson is not automatically a problem; identify the specific comprehension or completion risk.
Expected handoff
Return the path map, issue, evidence, severity, proposed correction, owner, and verification step. Group quick content fixes separately from structural or assessment decisions.
Questions this workflow answers
Can an agent walk through a course like a new learner and find the missing instructions, prerequisites, and broken progression points?
Yes. Give the agent the course, learner type, intended outcome, publishing state, and path it should audit. Heights Platform supplies the modules, lessons, resources, assessments, and progression context available to the account. The agent can map the journey from enrollment to completion and record where a learner lacks the information or access needed for the next step.
The audit should test more than links. It can check whether lesson titles and instructions agree, a resource appears before it is needed, an assessment covers taught material, and a completion condition allows progression as intended. Cohort-specific or unpublished content should remain separate. A lesson that looks missing to one learner may be intentionally gated, so the agent needs the access and prerequisite rules.
Each finding should include the course location, learner state, observed problem, likely consequence, and verification step. The agent should distinguish factual defects from editorial judgment. A broken resource is reproducible; a confusing explanation needs examples and review from the course owner. It should not rewrite instructional content or change publication status during the audit.
The handoff can group quick corrections, structural decisions, assessment questions, and access problems, with an owner for each. A course designer validates the intended pedagogy and approves changes. The result is a path-based review that shows what prevents progress and where the platform evidence ends, rather than a generic critique based only on reading individual lessons in isolation.
Testing should use representative learner states. A new enrollee, someone returning after a prerequisite, and a learner near completion may see different content or progression controls. The agent records the state behind each observation and checks whether links, downloads, instructions, assessment feedback, and completion rules agree at that point. It cannot call a lesson inaccessible or missing without confirming whether a deliberate gate, cohort rule, or publishing status explains the difference.